scholarly journals A Review on Map-Merging Methods for Typical Map Types in Multiple-Ground-Robot SLAM Solutions

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6988
Author(s):  
Shuien Yu ◽  
Chunyun Fu ◽  
Amirali K. Gostar ◽  
Minghui Hu
Keyword(s):  
Multiple Robots ◽  
Working Mechanism ◽  
Topological Maps ◽  
Occupancy Grid ◽  
Localization And Mapping ◽  
Robot Systems ◽  
Feature Based ◽  
Occupancy Grid Maps ◽  
Multi Robot ◽  
Map Merging

When multiple robots are involved in the process of simultaneous localization and mapping (SLAM), a global map should be constructed by merging the local maps built by individual robots, so as to provide a better representation of the environment. Hence, the map-merging methods play a crucial rule in multi-robot systems and determine the performance of multi-robot SLAM. This paper looks into the key problem of map merging for multiple-ground-robot SLAM and reviews the typical map-merging methods for several important types of maps in SLAM applications: occupancy grid maps, feature-based maps, and topological maps. These map-merging approaches are classified based on their working mechanism or the type of features they deal with. The concepts and characteristics of these map-merging methods are elaborated in this review. The contents summarized in this paper provide insights and guidance for future multiple-ground-robot SLAM solutions.

scholarly journals Tomographic Feature-Based Map Merging for Multi-Robot Systems

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 107 ◽  
Author(s):  
Heoncheol Lee
Keyword(s):  
Gaussian Mixture Models ◽  
Search Space ◽  
Gaussian Mixture ◽  
Transformation Matrix ◽  
Robot Systems ◽  
Feature Based ◽  
The Individual ◽  
Merging Method ◽  
Multi Robot ◽  
Map Merging

Multi-robot systems require collective map information on surrounding environments to efficiently cooperate with one another on assigned tasks. This paper addresses the problem of grid map merging to obtain the collective map information in multi-robot systems with unknown initial poses. If inter-robot measurements are not available, the only way to merge the maps is to find and match the overlapping area between maps. This paper proposes a tomographic feature-based map merging method, which can be successfully conducted with relatively small overlapping areas. The first part of the proposed method is to estimate a map transformation matrix using the Radon transform which can extract tomographically salient features from individual grid maps. The second part is to determine the search space using Gaussian mixture models based on the estimated map transformation matrix. The final part is to optimize an objective function modeled from tomographic information within the determined search space. Evaluation results with various pairs of individual maps produced by simulations and experiments showed that the proposed method can merge the individual maps more accurately than other map merging methods.

scholarly journals Grid Map Merging with Ant Colony Optimization for Multi-Robot Systems

2021 ◽  
Author(s):  
Heoncheol Lee
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Working Environment ◽  
Grid Map ◽  
Localization And Mapping ◽  
Robot Systems ◽  
The Individual ◽  
Merging Algorithms ◽  
Multi Robot ◽  
Map Merging

Multi-robot systems have recently been in the spotlight in terms of efficiency in performing tasks. However, if there is no map in the working environment, each robot must perform SLAM which simultaneously performs localization and mapping the surrounding environments. To operate the multi-robot systems efficiently, the individual maps should be accurately merged into a collective map. If the initial correspondences among the robots are unknown or uncertain, the map merging task becomes challenging. This chapter presents a new approach to accurately conducting grid map merging with the Ant Colony Optimization (ACO) which is one of the well-known sampling-based optimization algorithms. The presented method was tested with one of the existing grid map merging algorithms and showed that the accuracy of grid map merging was improved by the ACO.

A Multi-Robot System Using Mobile Agents with Ant Colony Clustering

2010 ◽  
pp. 174-192
Author(s):  
Yasushi Kambayashi ◽  
Yasuhiro Tsujimura ◽  
Hidemi Yamachi ◽  
Munehiro Takimoto
Keyword(s):  
Communication Networks ◽  
Mobile Agents ◽  
Software Agents ◽  
Ant Colony ◽  
Multiple Robots ◽  
Robot System ◽  
Mobile Software ◽  
Robot Systems ◽  
Ant Colony Clustering ◽  
Multi Robot

This chapter presents a framework using novel methods for controlling mobile multiple robots directed by mobile agents on a communication networks. Instead of physical movement of multiple robots, mobile software agents migrate from one robot to another so that the robots more efficiently complete their task. In some applications, it is desirable that multiple robots draw themselves together automatically. In order to avoid excessive energy consumption, we employ mobile software agents to locate robots scattered in a field, and cause them to autonomously determine their moving behaviors by using a clustering algorithm based on the Ant Colony Optimization (ACO) method. ACO is the swarm-intelligence-based method that exploits artificial stigmergy for the solution of combinatorial optimization problems. Preliminary experiments have provided a favorable result. Even though there is much room to improve the collaboration of multiple agents and ACO, the current results suggest a promising direction for the design of control mechanisms for multi-robot systems. In this chapter, we focus on the implementation of the controlling mechanism of the multi-robot system using mobile agents.

scholarly journals The Characteristics of the Map Merging Methods: A Survey

2010 ◽  
Vol 41 (1) ◽  
pp. 113-121 ◽  
Author(s):  
Ilze Andersone
Keyword(s):  
Artificial Intelligence ◽  
State Of The Art ◽  
Research Field ◽  
Autonomous Mobile Robots ◽  
Multiple Robots ◽  
Characteristic Set ◽  
Robotic Mapping ◽  
New Research ◽  
Multi Robot ◽  
Map Merging

The Characteristics of the Map Merging Methods: A SurveyThe development of the autonomous mobile robots is a popular field of research in the artificial intelligence for more than twenty years. An important prerequisite for creation of autonomous robot is the ability to create a map of the environment. However there are several problems in the robotic mapping that are still not completely solved. The use of multiple robots for mapping solves some of these problems, but in this case several new problems, specific to multi-robot mapping, arise. One of the problems in multi-robot mapping is merging all of the local maps that robots have created into one global map. The application of the robot teams in the exploration is a relatively new research field and initially methods for multi-robot mapping were just extended single robot mapping methods. Consequently the map merging problem was simplified. The research of map merging has only recently evolved, thus existing papers on the map merging describe specific map merging methods and there are no comprehensive surveys on the state of the art in the field of map merging. Therefore the goal of this paper is to describe the situation in the field of the map merging and to identify the main characteristics of the map merging methods. The interrelations of these characteristics can then be identified and the corresponding map merging approaches of each characteristic set named.

scholarly journals The influence of the map merging order on the resulting global map in multi-robot mapping

2012 ◽  
Vol 13 (1) ◽  
pp. 22-28
Author(s):  
Ilze Andersone
Keyword(s):  
Autonomous Robot ◽  
Multiple Robots ◽  
Important Prerequisite ◽  
Robot Teams ◽  
Mapping Process ◽  
As If ◽  
Multi Robot ◽  
Map Merging

An important prerequisite for creation of an autonomous robot is the ability to create the map of the environment. While the use of robot teams becomes more and more widely used, the issue of robot coordination becomes one of the central questions to be addressed. If multiple robots are used for the exploration of the environment, their collected information has to be fused into one general global map. This problem is called map merging. In the case, when more than two robots map the environment, it is possible that the order of map merging can influence the quality of the result - the global map. However, most researches in the map merging field address the problem as if the recommended order of map merging were known. The goal of this paper is to prove that the merging order can greatly influence the resulting global map and discuss the consequences this knowledge makes in the mapping process.

scholarly journals Swarm SLAM: Challenges and Perspectives

2021 ◽  
Vol 8 ◽  
Author(s):  
Miquel Kegeleirs ◽  
Giorgio Grisetti ◽  
Mauro Birattari
Keyword(s):  
Fault Tolerant ◽  
Point Of View ◽  
Decentralized System ◽  
Robot Swarm ◽  
Cost Constraints ◽  
Localization And Mapping ◽  
Robot Systems ◽  
The Moment ◽  
Hostile Environments ◽  
Multi Robot

A robot swarm is a decentralized system characterized by locality of sensing and communication, self-organization, and redundancy. These characteristics allow robot swarms to achieve scalability, flexibility and fault tolerance, properties that are especially valuable in the context of simultaneous localization and mapping (SLAM), specifically in unknown environments that evolve over time. So far, research in SLAM has mainly focused on single- and centralized multi-robot systems—i.e., non-swarm systems. While these systems can produce accurate maps, they are typically not scalable, cannot easily adapt to unexpected changes in the environment, and are prone to failure in hostile environments. Swarm SLAM is a promising approach to SLAM as it could leverage the decentralized nature of a robot swarm and achieve scalable, flexible and fault-tolerant exploration and mapping. However, at the moment of writing, swarm SLAM is a rather novel idea and the field lacks definitions, frameworks, and results. In this work, we present the concept of swarm SLAM and its constraints, both from a technical and an economical point of view. In particular, we highlight the main challenges of swarm SLAM for gathering, sharing, and retrieving information. We also discuss the strengths and weaknesses of this approach against traditional multi-robot SLAM. We believe that swarm SLAM will be particularly useful to produce abstract maps such as topological or simple semantic maps and to operate under time or cost constraints.

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